CSC 466 Winter 2020 Schedule

Table of contents

  1. Syllabus
  2. Schedule
  3. Technology
  4. Project

Schedule

Below is our tentative schedule. It is subject to change, but any changes will be reflected here.

Important Dates

  • January 6 (Monday) - Classes begin
  • January 20 (Monday) - Martin Luther King, Jr.’s birthday observed (no class)
  • January 21 (Tuesday) - Classes follow a Monday schedule (I assume this means no class for us)
  • March 13 (Friday) - Last day of classes
  • March 16-20 (Monday - Friday) - Final exams

Week 1 (1/7 and 1/9)

About Me

Go over Syllabus

Icebreaker

Chapter 1 (Intro) and Chapter 2 (Preliminaries)

Bayesian Classification Slides

Lab 1 - Python, NumPy, and Pandas

Week 2 (1/14 and 1/16)

Bayesian Classifier Slides

Week 2 - Chapter 12 - Learning with Trees

Lab 2 - Bayesian Classifier and Report

Week 3 (1/23)

No class on Tuesday

Chapter 13 - Decision by Committee

Continue Lab 2 - Bayesian Classifier and Report

Week 4 (1/28 and 1/30)

Chpater 13 - Decision by Committee

Lab 3 - Decision Tree and Report

Week 5 (2/4 and 2/6)

Chapter 3 - Neurons, Neural Networks, and Linear Discriminants

Lab 4 - Decision by Committee and Report

Thursday 2/6 - Exam 1

Week 6 (2/11 and 2/13)

Chapter 14 - Unsupervised Learning - Kmeans (supplemented by Hierarchical clustering notes)

Lab 5 - Perceptron and Report

Week 7 (2/18 and 2/20)

Assignment of project

Chapter 6 - Dimensionality reduction

Lab 6 - Clustering and Report

Week 8 (2/25 and 2/27)

Collaborative Filtering

Lab 7 - Dimensionality reduction and report

Week 9 (3/3 and 3/5)

Graph Algorithms (https://learning.oreilly.com/library/view/graph-algorithms/9781492047674/)

Lab 8 - Collaborative Filtering and Report

Week 10 (3/10 and 3/12)

Lab 9 - Graph Algorithms and Page Rank and Report

Lab 10 - Ethics of KDD sourced opinion column (Examples)

Thursday 3/12 - Exam 2